arXiv Machine Learning June 17, 2026 By Hengzhi He, Shirong Xu, Guang Cheng

Recursive Learning Without Collapse: A Weighting-Based Stabilization Framework

Read the original on arXiv Machine Learning →

arXiv:2502. 18049v5 Announce Type: replace-cross Abstract: Recent studies identified an intriguing phenomenon in recursive generative model training known as model collapse, where models trained on data generated by previous models exhibit severe performance degradation.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jul 17

Escaping Model Collapse via Synthetic Data Verification: Near-term Improvements and Long-term Convergence

arXiv:2510. 16657v3 Announce Type: replace-cross Abstract: Synthetic data has been increasingly used to train frontier generative models.

By Bingji Yi, Qiyuan Liu, Yuwei Cheng, Haifeng Xu

arXiv Machine Learning

Jun 15

Recursively Trained Diffusion Models: Limiting Collapse Distribution and Spectral Characterization

arXiv:2606. 13796v1 Announce Type: cross Abstract: Recursive training of generative models on their own outputs can lead to model collapse, a compounding drift away from the true data distribution.

By Na\"il B. Khelifa, Richard E. Turner, Ramji Venkataramanan

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arXiv AI

Jun 16

Quantifying the Impact of Lossy Compression on Neural Generative Surrogate Modeling

arXiv:2606. 15959v1 Announce Type: cross Abstract: Neural networks are used as generative surrogate models for scientific discovery, which are trainable approximations of scientific simulations.

By Zhimin Li, Harshitha Menon, Charles Jekel, Valerio Pascucci, Peter Lindstrom

arXiv Machine Learning

Jun 16

Discriminative Span as a Predictor of Synthetic Data Utility via Classifier Reconstruction

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rag computer-vision data-engineering

arXiv AI

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Efficient Weighted Sampling via Score-based Generative Models

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By Heasung Kim, Taekyun Lee, Hyeji Kim, Gustavo de Veciana

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arXiv Machine Learning

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From Fake to Real: Pretraining on Balanced Synthetic Images to Prevent Spurious Correlations in Image Recognition

arXiv:2308. 04553v4 Announce Type: replace-cross Abstract: Visual recognition models are prone to learning spurious correlations induced by a biased training set where certain conditions $B$ (\eg, Indoors) are over-represented in certain classes $Y$ (\eg, Big Dogs).

By Maan Qraitem, Kate Saenko, Bryan A. Plummer

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Escaping Model Collapse via Synthetic Data Verification: Near-term Improvements and Long-term Convergence

Recursively Trained Diffusion Models: Limiting Collapse Distribution and Spectral Characterization

Quantifying the Impact of Lossy Compression on Neural Generative Surrogate Modeling

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From Fake to Real: Pretraining on Balanced Synthetic Images to Prevent Spurious Correlations in Image Recognition